
'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__											=logging.get_logger(__name__)

lowercase__											=torch.device('cpu')






def      UpperCamelCase_				(      ):
	a_      				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	a_      				=						Image.open(requests.get(lowerCamelCase_  ,      stream=lowerCamelCase_						).raw						)
	return im






def      UpperCamelCase_				(      A__						):
	if swiftformer_name == "swiftformer_xs":
		return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]						)

	elif swiftformer_name == "swiftformer_s":
		return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]						)

	elif swiftformer_name == "swiftformer_l1":
		return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]						)

	elif swiftformer_name == "swiftformer_l3":
		return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						dct.pop(lowerCamelCase_						)
	a_      				=						val






def      UpperCamelCase_				(      A__						):
	a_      				=						[]
	for k in state_dict.keys():
		a_      				=						k
		if ".pwconv" in k:
			a_      				=						k_new.replace(""".pwconv"""  ,      """.point_wise_conv"""						)
		if ".dwconv" in k:
			a_      				=						k_new.replace(""".dwconv"""  ,      """.depth_wise_conv"""						)
		if ".Proj." in k:
			a_      				=						k_new.replace(""".Proj."""  ,      """.proj."""						)
		if "patch_embed" in k_new:
			a_      				=						k_new.replace("""patch_embed"""  ,      """swiftformer.patch_embed.patch_embedding"""						)
		if "network" in k_new:
			a_      				=						k_new.split("""."""						)
			if ls[2].isdigit():
				a_      				=						'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]						)
			else:
				a_      				=						k_new.replace("""network"""  ,      """swiftformer.encoder.network"""						)
		rename_keys.append((k, k_new)						)
	return rename_keys






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	a_      				=						10_00
	a_      				=						'''huggingface/label-files'''
	a_      				=						'''imagenet-1k-id2label.json'''
	a_      				=						json.load(open(hf_hub_download(lowerCamelCase_  ,      lowerCamelCase_  ,      repo_type="""dataset"""						)  ,      """r"""						)						)
	a_      				=						{int(lowerCamelCase_						): v for k, v in idalabel.items()}
	a_      				=						idalabel
	a_      				=						{v: k for k, v in idalabel.items()}

	# size of the architecture
	if swiftformer_name == "swiftformer_xs":
		a_      				=						[3, 3, 6, 4]
		a_      				=						[48, 56, 1_12, 2_20]

	elif swiftformer_name == "swiftformer_s":
		a_      				=						[3, 3, 9, 6]
		a_      				=						[48, 64, 1_68, 2_24]

	elif swiftformer_name == "swiftformer_l1":
		a_      				=						[4, 3, 10, 5]
		a_      				=						[48, 96, 1_92, 3_84]

	elif swiftformer_name == "swiftformer_l3":
		a_      				=						[4, 4, 12, 6]
		a_      				=						[64, 1_28, 3_20, 5_12]

	# load state_dict of original model, remove and rename some keys
	if original_ckpt:
		if original_ckpt.startswith("""https"""						):
			a_      				=						torch.hub.load_state_dict_from_url(lowerCamelCase_  ,      map_location="""cpu"""  ,      check_hash=lowerCamelCase_						)
		else:
			a_      				=						torch.load(lowerCamelCase_  ,      map_location="""cpu"""						)
	a_      				=						checkpoint

	a_      				=						create_rename_keys(lowerCamelCase_						)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(lowerCamelCase_  ,      lowerCamelCase_  ,      lowerCamelCase_						)

	# load HuggingFace model
	a_      				=						SwiftFormerForImageClassification(lowerCamelCase_						).eval()
	hf_model.load_state_dict(lowerCamelCase_						)

	# prepare test inputs
	a_      				=						prepare_img()
	a_      				=						ViTImageProcessor.from_pretrained("""preprocessor_config"""						)
	a_      				=						processor(images=lowerCamelCase_  ,      return_tensors="""pt"""						)

	# compare outputs from both models
	a_      				=						get_expected_output(lowerCamelCase_						)
	a_      				=						hf_model(inputs["""pixel_values"""]						).logits

	assert hf_logits.shape == torch.Size([1, 10_00]						)
	assert torch.allclose(hf_logits[0, 0:5]  ,      lowerCamelCase_  ,      atol=1e-3						)

	Path(lowerCamelCase_						).mkdir(exist_ok=lowerCamelCase_						)
	print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''						)
	hf_model.save_pretrained(lowerCamelCase_						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--swiftformer_name',
		    default='swiftformer_xs',
		    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
		    type=str,
		    help='Name of the SwiftFormer model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='./converted_outputs/',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

		lowercase__											=parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowercase			:				Optional[int]   =     logging.getLogger(__name__)

lowercase			:				str   =     "pytorch_model.bin"







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}		,       )







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}				)
							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''A csv or a json file containing the validation data.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The name of the task to train on.'''}		,       )
							UpperCAmelCase_						:		Optional[List[str]]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The list of labels for the task.'''}				)







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''accuracy'''		,       metadata={'''help''': '''The evaluation metric used for the task.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''no'''		,       metadata={
							        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
							    }		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=10		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={
							        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
							    }		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=1_00		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Random seed for initialization.'''}		,       )






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					datasets.concatenate_datasets([infer_input, infer_output]     ,				axis=1		)

							if args.do_filter_by_confidence:
														A      :					Union[str, Any]      		=					dataset.filter(lambda lowerCamelCase_		: example["probability"] > args.confidence_threshold		)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														A      :					Optional[int]      		=					int(eval_result * len(lowerCamelCase_		)		)
														print(lowerCamelCase_		)
														A      :					Any      		=					dataset.sort('''probability'''     ,				reverse=lowerCamelCase_		)
														A      :					Any      		=					dataset.select(range(lowerCamelCase_		)		)

							A      :					Dict      		=					dataset.remove_columns(['''label''', '''probability''']		)
							A      :					List[str]      		=					dataset.rename_column('''prediction'''     ,				'''label'''		)
							A      :					Tuple      		=					dataset.map(lambda lowerCamelCase_		: {"label": idalabel[example["label"]]}		)
							A      :					Dict      		=					dataset.shuffle(seed=args.seed		)

							A      :					str      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)
							if args.data_file_extension == "csv":
														dataset.to_csv(lowerCamelCase_     ,				index=lowerCamelCase_		)
							else:
														dataset.to_json(lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				**lowerCamelCase_		):
							A      :					Optional[Any]      		=					Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				)
							logger.info(accelerator.state		)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							A      :					List[str]      		=					STModelArguments(model_name_or_path=lowerCamelCase_		)
							A      :					List[Any]      		=					STDataArguments(train_file=lowerCamelCase_     ,				infer_file=lowerCamelCase_		)
							A      :					int      		=					STTrainingArguments(output_dir=lowerCamelCase_		)
							A      :					Any      		=					argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(lowerCamelCase_		).items():
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							for key, value in kwargs.items():
														if hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

    # Sanity checks
							A      :					Tuple      		=					{}
							A      :					str      		=					None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							A      :					Dict      		=					args.train_file
							A      :					Any      		=					args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														A      :					Optional[int]      		=					args.eval_file

							for key in data_files:
														A      :					str      		=					data_files[key].split('''.'''		)[-1]
														assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
														if args.data_file_extension is None:
																					A      :					List[Any]      		=					extension
														else:
																					assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed		)

							logger.info('''Creating the initial data directory for self-training...'''		)
							A      :					Any      		=					F'{args.output_dir}/self-train_iter-{{}}'.format
							A      :					Tuple      		=					data_dir_format(0		)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir     ,				exist_ok=lowerCamelCase_		)
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
							accelerator.wait_for_everyone()

							A      :					str      		=					None
							A      :					List[str]      		=					None
							A      :					List[str]      		=					0
							A      :					List[Any]      		=					False
							# Show the progress bar
							A      :					Any      		=					tqdm(range(args.max_selftrain_iterations		)     ,				disable=not accelerator.is_local_main_process		)

							# Self-train
							for iteration in range(0     ,				int(args.max_selftrain_iterations		)		):
														A      :					Union[str, Any]      		=					data_dir_format(lowerCamelCase_		)
														assert os.path.exists(lowerCamelCase_		)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''stage-1'''		)
														A      :					Optional[Any]      		=					{
														    '''accelerator''': accelerator,
														    '''model_name_or_path''': args.model_name_or_path,
														    '''cache_dir''': args.cache_dir,
														    '''do_train''': True,
														    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
														    '''do_eval''': True if args.eval_file is not None else False,
														    '''eval_file''': data_files['''eval'''],
														    '''do_predict''': True,
														    '''infer_file''': data_files['''infer'''],
														    '''task_name''': args.task_name,
														    '''label_list''': args.label_list,
														    '''output_dir''': current_output_dir,
														    '''eval_metric''': args.eval_metric,
														    '''evaluation_strategy''': args.evaluation_strategy,
														    '''early_stopping_patience''': args.early_stopping_patience,
														    '''early_stopping_threshold''': args.early_stopping_threshold,
														    '''seed''': args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																												arguments_dict.update({key: value}		)

														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
														if os.path.exists(lowerCamelCase_		):
																					logger.info(
																					    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
														else:
																					logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''     ,				lowerCamelCase_		)
																					finetune(**lowerCamelCase_		)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(lowerCamelCase_		)
																					logger.info('''Self-training job completed: iteration: %d, stage: 1.'''     ,				lowerCamelCase_		)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)
																					A      :					int      		=					os.path.join(lowerCamelCase_     ,				'''stage-2'''		)
																					# Update arguments_dict
																					A      :					int      		=					model_path
																					A      :					Optional[Any]      		=					data_files['''train''']
																					A      :					Tuple      		=					current_output_dir

																					A      :					List[str]      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
																					if os.path.exists(lowerCamelCase_		):
																												logger.info(
																												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
																					else:
																												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''     ,				lowerCamelCase_		)
																												finetune(**lowerCamelCase_		)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(lowerCamelCase_		)
																												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''     ,				lowerCamelCase_		)

														A      :					List[Any]      		=					iteration
														A      :					Optional[Any]      		=					data_dir_format(iteration + 1		)

														A      :					Tuple      		=					AutoConfig.from_pretrained(os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)		)
														A      :					List[Any]      		=					config.idalabel
														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				'''eval_results_best-checkpoint.json'''		)
														A      :					Union[str, Any]      		=					os.path.join(lowerCamelCase_     ,				'''test_results_best-checkpoint.json'''		)
														assert os.path.exists(lowerCamelCase_		)

														with open(lowerCamelCase_     ,				'''r'''		) as f:
																					A      :					Optional[int]      		=					float(json.load(lowerCamelCase_		)[args.eval_metric]		)
														A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''infer_output_best-checkpoint.csv'''		)
														assert os.path.exists(lowerCamelCase_		)
														# Loading the dataset from local csv or json files.
														A      :					str      		=					load_dataset(args.data_file_extension     ,				data_files={'''data''': data_files['''infer''']}		)['''data''']
														A      :					Tuple      		=					load_dataset('''csv'''     ,				data_files={'''data''': infer_output_file}		)['''data''']

														if accelerator.is_main_process:
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
																					shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)		)
																					if os.path.exists(lowerCamelCase_		):
																												shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'test_results_iter-{iteration}.json'		)		)
																					create_pseudo_labeled_data(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()

														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A      :					Any      		=					eval_result

																					if best_iteration is None:
																												A      :					int      		=					new_iteration
																												A      :					Optional[Any]      		=					new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			A      :					Optional[int]      		=					new_iteration
																																			A      :					Any      		=					new_eval_result
																																			A      :					Any      		=					0
																												else:
																																			if new_eval_result == best_eval_result:
																																										A      :					Any      		=					new_iteration
																																										A      :					Any      		=					new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A      :					int      		=					True

														progress_bar.update(1		)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info('''Best iteration: %d'''     ,				lowerCamelCase_		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)
							else:
														# Assume that the last iteration is the best
														logger.info('''Best iteration: %d'''     ,				args.max_selftrain_iterations - 1		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)






